Determining spatial and temporal scales for management: lessons from whaling

Selection of the appropriate management unit is critical to the conservation of animal populations. Defining such units depends upon knowledge of population structure and upon the timescale being considered. Here, we examine the trajectory of eleven subpopulations of five species of baleen whales to investigate temporal and spatial scales in management. These subpopulations were all extirpated by commercial whaling, and no recovery or repopulation has occurred since. In these cases, time elapsed since commercial extinction ranges from four decades to almost four centuries. We propose that these subpopulations did not recover either because cultural memory of the habitat has been lost, because widespread whaling among adjacent stocks eliminated these as sources for repopulation, and/or because segregation following exploitation produced the abandonment of certain areas. Spatial scales associated with the extirpated subpopulations are frequently smaller than those typically employed in management. Overall, the evidence indicates that: (1) the time frame for management should be at most decadal in scope (i.e., <100 yr) and based on both genetic and nongenetic evidence of population substructure, and (2) at least some stocks should be defined on a smaller spatial scale than they currently are.

Historical Observations of Humpback And Blue Whales in the North Atlantic Ocean: Clues to Migratory Routes and Possibly Additional Feeding Grounds

The seasonal distributions of humpback and blue whales (Megaptera novaeangliae and Balaenoptera musculus, respectively) in the North Atlantic Ocean are not fully understood. Although humpbacks have been studied intensively in nearshore or coastal feeding and breeding areas, their migratory movements between these areas have been largely inferred. Blue whales have only been studied intensively along the north shore of the Gulf of St. Lawrence, and their seasonal occurrence and movements elsewhere in the North Atlantic are poorly known. We investigated the historical seasonal distributions of these two species using sighting and catch data extracted from American 18th and 19th century whaling logbooks. These data suggest that humpback whales migrated seasonally from low-latitude calving/ breeding grounds over a protracted period, and that some of them traveled far offshore rather than following coastal routes. Also, at least some humpbacks apparently fed early in the summer west of the Mid-Atlantic Ridge, well south of their known present-day feeding grounds. In assessing the present status of the North Atlantic humpback population, it will be important to determine whether such offshore feeding does in fact occur. Blue whales were present across the southern half of the North Atlantic during the autumn and winter months, and farther north in spring and summer, but we had too few data points to support inferences about these whales’ migratory timing and routes.

Seasonal home ranges and fidelity to breeding sites among ringed seals

Population structure and patterns of habitat use among ringed seals (Phoca hispida) are poorly known, in part because seasonal movements have not been adequately documented. We monitored the movements of 98 ringed seals in the Beaufort and Chukchi seas between 1990 and 2006 using three forms of telemetry. In the winter—spring period (when the seals were occupying shorefast ice), we used radio and ultra-sonic tags to track movements above and below the ice, respectively. We used satellite-linked transmitters in summer and fall (when the seals ranged away from their winter sites) to track at-sea movements. In the shorefast ice habitat, the home ranges of 27 adult males ranged from\1 to 13.9 km2 (median = 0.628) while the home ranges of 28 adult females ranged from \1 to 27.9 km2 (median = 0.652). The 3-dimensional volumes used by 9 seals tracked acoustically under the ice averaged 0.07 (SD = 0.04) km3 for subadults and adult males and 0.13 (SD = 0.04) km3 for adult females. Three of the radio-tracked seals and 9 tracked by satellite ranged up to 1,800 km from their winter/spring home ranges in summer but returned to the same small (1–2 km2) sites during the ice-bound months in the following year. The restricted movements of ringed seals during the ice-bound season— including the breeding season—limits their foraging activities for most of the year and may minimize gene flow within the species.

Eastern temperate North Pacific offshore killer whales (Orcinus orca): Occurrence, movements, and insights into feeding ecology

Beginning in the late 1980s, large groups of previously unidentified killer whales (Orcinus orca) were sighted off the west coast of Vancouver Island and in the Queen Charlotte Islands, British Columbia. Scientists working in this region produced two killer whale photo-identification catalogues that included both transient (mammal-eating) whales and 65 individual whales that investigators believed represented a distinct killer whale community (Ford et al. 1992, Heise et al. 1993). It was thought that these killer whales maintained a generally offshore distribution and were provisionally termed “offshores”; a term that has since been used as a population identifier for the eastern temperate North Pacific offshore killer whale population. Then in September 1992, 75 unidentified whales entered the Strait of Juan de Fuca just south and east of Victoria, British Columbia (Walters et al. 1992). Although most of these whales had not been seen before, two were matched to killer whales in the Queen Charlotte photo-identification catalogue (Ford et al. 1992, Heise et al. 1993) and were thus listed as “offshore” killer whales. During a similar time period, other large groups of killer whales, previously unidentified, were also being sighted off Alaska and California (Dahlheim et al. 1997; Nancy Black and Alisa Schulman- Janiger, unpublished data, respectively).

Killer Whales and Marine Mammal Trends in The North Pacific—A Re-Examination of Evidence for Sequential Megafauna Collapse and the Prey-Switching Hypothesis

Springer et al. (2003) contend that sequential declines occurred in North Pacific populations of harbor and fur seals, Steller sea lions, and sea otters. They hypothesize that these were due to increased predation by killer whales, when industrial whaling’s removal of large whales as a supposed primary food source precipitated a prey switch. Using a regional approach, we reexamined whale catch data, killer whale predation observations, and the current biomass and trends of potential prey, and found little support for the prey-switching hypothesis. Large whale biomass in the Bering Sea did not decline as much as suggested by Springer et al., and much of the reduction occurred 50–100 yr ago, well before the declines of pinnipeds and sea otters began; thus, the need to switch prey starting in the 1970s is doubtful. With the sole exception that the sea otter decline followed the decline of pinnipeds, the reported declines were not in fact sequential. Given this, it is unlikely that a sequential megafaunal collapse from whales to sea otters occurred. The spatial and temporal patterns of pinniped and sea otter population trends are more complex than Springer et al. suggest, and are often inconsistent with their hypothesis. Populations remained stable or increased in many areas, despite extensive historical whaling and high killer whale abundance. Furthermore, observed killer whale predation has largely involved pinnipeds and small cetaceans; there is little evidence that large whales were ever a major prey item in high latitudes. Small cetaceans (ignored by Springer et al.) were likely abundant throughout the period. Overall, we suggest that the Springer et al. hypothesis represents a misleading and simplistic view of events and trophic relationships within this complex marine ecosystem.

Use of chemical tracers in assessing the diet and foraging regions of eastern North Pacific killer whales

Top predators in the marine environment integrate chemical signals acquired from their prey that reflect both the species consumed and the regions from which the prey were taken. These chemical tracers—stable isotope ratios of carbon and nitrogen; persistent organic pollutant (POP) concentrations, patterns and ratios; and fatty acid profiles—were measured in blubber biopsy samples from North Pacific killer whales (Orcinus orca) (n = 84) and were used to provide further insight into their diet, particularly for the offshore group, about which little dietary information is available. The offshore killer whales were shown to consume prey species that were distinctly different from those of sympatric resident and transient killer whales. In addition, it was confirmed that the offshores forage as far south as California. Thus, these results provide evidence that the offshores belong to a third killer whale ecotype. Resident killer whale populations showed a gradient in stable isotope profiles from west (central Aleutians) to east (Gulf of Alaska) that, in part, can be attributed to a shift from off-shelf to continental shelf-based prey. Finally, stable isotope ratio results, supported by field observations, showed that the diet in spring and summer of eastern Aleutian Island transient killer whales is apparently not composed exclusively of Steller sea lions.

Use of chemical tracers in assessing the diet and foraging regions of eastern North Pacific killer whales

Top predators in the marine environment integrate chemical signals acquired from their prey that reflect both the species consumed and the regions from which the prey were taken. These chemical tracers—stable isotope ratios of carbon and nitrogen; persistent organic pollutant (POP) concentrations, patterns and ratios; and fatty acid profiles—were measured in blubber biopsy samples from North Pacific killer whales (Orcinus orca) (n = 84) and were used to provide further insight into their diet, particularly for the offshore group, about which little dietary information is available. The offshore killer whales were shown to consume prey species that were distinctly different from those of sympatric resident and transient killer whales. In addition, it was confirmed that the offshores forage as far south as California. Thus, these results provide evidence that the offshores belong to a third killer whale ecotype. Resident killer whale populations showed a gradient in stable isotope profiles from west (central Aleutians) to east (Gulf of Alaska) that, in part, can be attributed to a shift from off-shelf to continental shelf-based prey. Finally, stable isotope ratio results, supported by field observations, showed that the diet in spring and summer of eastern Aleutian Island transient killer whales is apparently not composed exclusively of Steller sea lions.

Antler Size of Alaskan Moose Alces alces gigas: Effects of Population Density, Hunter Harvest and Use of Guides

Moose Alces alces gigas in Alaska, USA, exhibit extreme sexual dimorphism, with adult males possessing large, elaborate antlers. Antler size and conformation are influenced by age, nutrition and genetics, and these bony structures serve to establish social rank and affect mating success. Population density, combined with anthropogenic effects such as harvest, is thought to influence antler size. Antler size increased as densities of moose decreased, ostensibly a density-dependent response related to enhanced nutrition at low densities. The vegetation type where moose were harvested also affected antler size, with the largest-antlered males occupying more open habitats. Hunts with guides occurred in areas with low moose density, minimized hunter interference and increased rates of success. Such hunts harvested moose with larger antler spreads than did non-guided hunts. Knowledge and abilities allowed guides to satisfy demands of trophy hunters, who are an integral part of the Alaskan economy. Heavy harvest by humans was also associated with decreased antler size of moose, probably via a downward shift in the age structure of the population resulting in younger males with smaller antlers. Nevertheless, density-dependence was more influential than effects of harvest on age structure in determining antler size of male moose. Indeed, antlers are likely under strong sexual selection, but we demonstrate that resource availability influenced the distribution of these sexually selected characters across the landscape. We argue that understanding population density in relation to carrying capacity (K) and the age structure of males is necessary to interpret potential consequences of harvest on the genetics of moose and other large herbivores. Our results provide researchers and managers with a better understanding of variables that affect the physical condition, antler size, and perhaps the genetic composition of populations, which may be useful in managing and modeling moose populations.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

An animal movement model incorporating home range and habitat selection

Wildlife biologists are often interested in how an animal uses space and the habitat resources within that space. We propose a single model that estimates an animal’s home range and habitat selection parameters within that range while accounting for the inherent autocorrelation in frequently sampled telemetry data. The model is applied to brown bear telemetry data in southeast Alaska.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions

Over the past three decades, the decline and altered spatial distribution of the western stock of Steller sea lions (Eumetopias jubatus) in Alaska have been attributed to changes in the distribution or abundance of their prey due to the cumulative effects of fisheries and environmental perturbations. During this period, dietary prey occurrence and diet diversity were related to population decline within metapopulation regions of the western stock of Steller sea lions, suggesting that environmental conditions may be variable among regions. The objective of this study, therefore, was to examine regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions within the context of recent measures of diet diversity and population trajectories. Habitat use was assessed by deploying satellite-depth recorders and satellite relay data loggers on juvenile Steller sea lions (n = 45) over a five-year period (2000–2004) within four regions of the western stock, including the western, central, and eastern Aleutian Islands, and central Gulf of Alaska. Areas used by sea lions during summer months (June, July, and August) were demarcated using satellite telemetry data and characterized by environmental variables (sea surface temperature [SST] and chlorophyll a [chl a]), which possibly serve as proxies for environmental processes or prey. Spatial patterns of SST diversity and Steller sea lion population trends among regions were fairly consistent with trends reported for diet studies, possibly indicating a link between environmental diversity, prey diversity, and distribution or abundance of Steller sea lions. Overall, maximum spatial heterogeneity coupled with minimal temporal variability of SST appeared to be beneficial for Steller sea lions. In contrast, these patterns were not consistent for chl a, and there appeared to be an ecological threshold. Understanding how Steller sea lions respond to measures of environmental heterogeneity will ultimately be useful for implementing ecosystem management approaches and developing additional conservation strategies.

Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.